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Vectorisation: The Rise of Parallelism

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New challenges in the financial markets driven by changes in market structure and regulations and accounting rules like Basel III, EMIR, Dodd Frank, MiFID II, Solvency II, IFRS 13, IRFS 9, and FRTB, have increased demand for higher performance risk and analytics. Problems like XVA require orders of magnitude more calculations for accurate results. This demand for higher performance has put a focus on how to get the most out of the latest generation of hardware.

vectorisation is a key tool for dramatically improving the performance of code running on modern CPUs. Vectorisation is the process of converting an algorithm from operating on a single value at a time to operating on a set of values at one time. Modern CPUs provide direct support for vector operations where a single instruction is applied to multiple data (SIMD).

For the past decade, Moore’s law has continued to prevail, but while chip makers have continued to pack more transistors into every square inch of silicon, the focus of innovation has moved away from greater clock speeds and towards multicore and manycore architectures.

As Herb Sutter famously observed in 2005, for developers this architectural shift meant the end of the “Free Lunch,” where existing software  automatically ran faster with each new generation of hardware. Traditional applications based on a single serial thread of instructions no longer see performance gains from new hardware as CPU clock rates have flat-lined.

Since that time, a great deal of focus has been given to engineering applications that are capable of exploiting the growing number of CPU cores by running multi-threaded or grid-distributed calculations. This type of parallelism has become a routine part of designing performance critical  software.

 

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